Post on 06-Jan-2016
description
Web Usage Mining with Semantic Analysis
Date: 2013/12/18Author: Laura Hollink, Peter Mika, Roi BlancoSource: WWW’13Advisor: Jia-Ling KohSpeaker: Pei-Hao Wu
OutlineIntroductionMethod and EvaluationConclusion
IntroductionMotivation content publishers are interested in understanding user needs in order to select and structure the content of their properties
Search engines collect query log, while content providers log information about search referrals, site search
IntroductionWe aggregate the information
into sessions
IntroductionA key challenge is that query logs is the notable sparsity
because 64% percent of queries are unique within a year
So we have an idea that mining web log with semantic analysis
OutlineIntroductionMethod and EvaluationConclusion
Workflow Our proposed workflow for
semantic usage mining data collection and processing,
entity linking, filtering, pattern mining and learning
Data ProcessingCollected a sample of server logs of
Yahoo! Search in the United States from June, 2011
Limit the collected data to sessions about movie and sessions contain at least one visit to any of 16 popular movie sites
Collected 1.7 million session, containing over 5.8 million queries and over 6.8 million clicks
Data ProcessingApply the filtering of navigational queries
and we identify 117663 navigational queries, which makes it the 12th most frequent category of queries from all other semantic types
Definition 1 (Navigational Query). Given a query q that leads to a click on
webpage w, and given that q is linked to entity e, q is a “navigational query” if the webpage w is an offcial homepage of the entity e.
Entity LinkingLinking Queries to Entities
link the queries to entities of the semantic resources : Freebase
Choose the first result which is searched by adding “site:wikipedia.org” in Yahoo! Search to link queries to entities
Entity LinkingLinking Entities to Types
Use Freebase API to do it but it has some strange cases, e.g. for the entity “Arnold” the type bodybuilder is chosen as the most notable, rather than the more intuitive types politician or actor
Entity LinkingLinking Entities to Types
In order to improve this problem we have four rules: disregard internal and administrative types, e.g.
to denote which user is responsible prefer schema information in established domains
over user defined schemas aggregate specific types into more general types
all specific types of location are a location all specific types of award winners
always prefer the following list of movie related types over all other types: /film/film, /film/actor, /artist, /tv/tv_program, /tv/tv_actor
Entity LinkingDictionary Tagging
Label queries with a dictionary created from the top hundred most frequent words and we can capture the intent of the user regarding the entity.
The top twenty terms that appear in our dictionary are as follows: movie, movies, theater, cast, quotes,
free, theaters ,watch , 2011, new, tv, show, dvd, online, sex, video, cinema, trailer, list, theatre . . .
Entity LinkingEvaluation
Provide a rater with the queries and ask user to manually create links to Freebase concepts
Compare manually created < query, entity> and < entity, type> pairs to automatically created links
Entity LinkingEvaluation
50 most frequent queries and 50 random queries
50 most frequent entities and 50 random entities
Semantic Pattern Mining
Multi-query patternsUse the PrefixSpan algorithm and its
implementation in the open source SPMF toolkit
Semantic Pattern Mining
Multi-query patternsBy looking at the actual entities and
modifiers in queries, we find the user are looking for the same information about different entities
We can also filter our data using our indices to interesting subsets of sessions i.e. for new movies user are interested in the trailer while for old movies user are interested in cast
Semantic Pattern Mining
Multi-query patterns
Predicting Website Abandonment When the user navigate away from the website, we
can speak of users being lost
Definition 2 (Loosing query). Given a query q that leads to a click on website w, q is a “loosing query” if one of the following two session patterns occur:◦ 1. q1 - cw - q2 - co◦ 2. q1 - cw - co
where website o is different from website w, and q1 and q2 are linked to the same entity.
predict abandonment by Gradient Boosted Decision Tree(GBDT)
Predicting Website AbandonmentEvaluation
We want to predict that a user will be gained or lost for a particular website
There are three tasks addressed using supervised learning: Task 1 predict that a user will be gained or lost for
a given website. We use all features, including the click on the loosing website
Task 2 predict that a user will be gained or lost for a given website, excluding the loosing website as a feature
Task 3 predict whether a user will be gained or lost between two given websites
Predicting Website AbandonmentEvaluation
We report results in terms of area under the curve(AUC)
Total amount of around 150K sessions
The training and testing is performed using 10-fold cross-validation
Predicting Website AbandonmentEvaluation
OutlineIntroductionMethod and EvaluationConclusion
Conclusion
Our method depends on the availability of Linked Open Data on the topics of the queries
To analyze query patterns and predict website abandonment we first linked queries to entities and then generalized them to types
Further research is needed to verify whether other domain benefit from this type of analysis